Le rôle de la labellisation dans la construction d’un marché - Le cas de l'ISR en France (The Role of Labellisation in the Design of a Market: The Case of SRI in France)
Bibliographic record
Abstract
French Abstract: L’Investissement Socialement Responsable (ISR) en France reste peu developpe pour les investisseurs particuliers, en depit d’une croissance forte des fonds ISR et du lancement de labels a leur intention. L’objectif de cet article est de mieux comprendre le role limite des labels. Notre analyse s’appuie sur l’interaction entre trois elements: label et asymetrie d’information, choix des attributs informationnels des labels et objectifs des organismes porteurs de labellisation, concurrence induite entre labels. Deux facteurs expliquent l’impact limite des labels. D’une part, les attributs informationnels mis en evidence par les labels refletent plus le point de vue des societes de gestion que celui des investisseurs particuliers. D’autre part, la distribution de l’ISR aupres des particuliers passe majoritairement par les reseaux des banques et assurances, reseaux pour lesquels il ne constitue pas un veritable axe de differenciation concurrentielle. English Abstract: The attractiveness of SRI (Socially Responsible Investment) for retail investors in France has remained limited in spite of the launch of labeling schemes and a substantial growth of SRI funds. The article analyzes why the labeling impact has been limited. Our framework is based on the interaction of three elements: labels and information asymmetry, the labeling organizations and the selection of information attributes, the induced competition between labels. Two main factors explain the limited impact of labels. First, the information attributes disclosed by the labels reflect the viewpoint of asset managers rather than the one of retail investors. Second, the distribution of SRI by banking and insurance networks is not a factor of competitive advantage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".